Basic genetic map construction

Learning Goals

At the end of this exercise, you will be able to: 1. know the concept of F2 intercross and the standard input format for R/qtl 2. summarize and understand the F2 intercross data 3. construct basic genetic map using R/qtl 4. create interactive graphics for genetic mapping using R/qtlcharts

Resources and references

• R/qtl: A QTL mapping environment
• R/qtl manual
• R/qtlcharts Interactive Graphics for Quantitative Trait Locus Mapping
• R/qtlcharts

F2 intercross strategy

• Resource

Load the libraries

library(qtl)
library(qtlcharts)
knitr::opts_chunk$set(fig.width=8, fig.height=6, message=FALSE)

The function read.cross is for importing data into R/qtl.

?read.cross

Example 1: Identify genomic regions that regulate blood pressure, heart rate, and heart weight

We will consider data from Sugiyama et al., Physiol Genomics 10:5–12, 2002. The data are from an intercross between BALB/cJ and CBA/CaJ; only male offspring were considered. There are four phenotypes: blood pressure, heart rate, body weight, and heart weight. “sug.csv” is the name of the file, which we import directly from the R/qtl website. genotypes indicates the codes used for the genotypes; alleles indicates single-character codes to be used in plots and such.

sug <- read.cross("csv", "https://rqtl.org", "sug.csv",
                  genotypes=c("CC", "CB", "BB"), alleles=c("C", "B"))

##  --Read the following data:
##   163  individuals
##   93  markers
##   6  phenotypes
##  --Cross type: f2

Summaries

Get a quick summary of the data.

summary(sug)

##     F2 intercross
## 
##     No. individuals:    163 
## 
##     No. phenotypes:     6 
##     Percent phenotyped: 95.1 95.7 99.4 99.4 100 100 
## 
##     No. chromosomes:    19 
##         Autosomes:      1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 
## 
##     Total markers:      93 
##     No. markers:        5 7 5 5 5 4 8 4 4 5 6 3 3 5 5 4 4 6 5 
##     Percent genotyped:  98.3 
##     Genotypes (%):      CC:23.9  CB:50.2  BB:26.0  not BB:0.0  not CC:0.0

head(sug$pheno)

##    bp  hr   bw heart_wt sex mouse_ID
## 1 104 517 37.0      133   1        3
## 2 108 690 38.9      135   1        4
## 3 115 653 43.8      159   1        5
## 4 119 592 42.5      131   1        6
## 5  NA  NA 43.6      128   1        8
## 6 106 517 35.3      175   1        9

There are a number of simple functions for pulling out pieces of summary information.

nind(sug)    # No. individuals

## [1] 163

nphe(sug)    # No. phenotypes

## [1] 6

nchr(sug)    # No. chromosomes

## [1] 19

totmar(sug)  # Total markers

## [1] 93

nmar(sug)    # No. markers on each chromosome

##  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 
##  5  7  5  5  5  4  8  4  4  5  6  3  3  5  5  4  4  6  5

Get a summary plot of the data.

plot(sug)

Individual parts of the above plot may be obtained as follows.

#?plotMissing
plotMissing(sug, main="")

plotMissing(sug, main="", reorder=1)

#?plotMap
plotMap(sug)

plotMap(sug,show.marker.names=TRUE)

#?plotPheno
plotPheno(sug, pheno.col=1)

plotPheno(sug, pheno.col=2)

plotPheno(sug, pheno.col=3)

plotPheno(sug, pheno.col=4)

Let’s make an interactive chart of the genetic map of markers for these data using R/qtlcharts.

iplotMap(sug) #D7MIT31

Let’s use iplotCorr to plot a heat map of the correlation matrix for the phenotype of all pairs, linked to scatterplots of the phenotypes.

iplotCorr(sug$pheno)

## Warning in stats::cor(mat, use = "pairwise.complete.obs"): the standard
## deviation is zero

iplotCorr(sug$pheno, chartOpts=list(height=300, width=600, scatcolors="pink"))

## Warning in stats::cor(mat, use = "pairwise.complete.obs"): the standard
## deviation is zero

The interactive graphs produced by R/qtlcharts are, by default, saved to a temporary file and then opened in the default web browser. If you want to save a chart to a particular file, assign the result to some object and use the function saveWidget() in the [htmlwidgets package] (http://www.htmlwidgets.org/), as follows:

corrplot <- iplotCorr(sug$pheno, chartOpts=list(height=300, width=600, scatcolors="pink"))

## Warning in stats::cor(mat, use = "pairwise.complete.obs"): the standard
## deviation is zero

htmlwidgets::saveWidget(corrplot, file="iplotCorr_example.html")

Quantitative Trait Locus (QTL) Analysis

Quantitative trait locus (QTL) analysis is a statistical method that links two types of information—phenotypic data (trait measurements) and genotypic data (usually molecular markers)—in an attempt to explain the genetic basis of variation in complex traits (Falconer & Mackay, 1996; Kearsey, 1998; Lynch & Walsh, 1998). Quantitative Trait Locus (QTL) Analysis

LOD Score

LOD score is actually an acronym for “log of the odds,” LOD. LOD score actually refers to a numerical result when estimating whether two genes, or a gene and a disease, are linked to one another. NIH LOD Score

Single-QTL analysis

We first calculate the QTL genotype probabilities, given the observed marker data, via the function calc.genoprob. This is done at the markers and at a grid along the chromosomes. The argument step is the density of the grid (in cM), and defines the density of later QTL analyses. To perform a single-QTL genome scan, we use the function scanone. By default, it considers the first phenotype in the input cross object (in this case, blood pressure).

sug <- calc.genoprob(sug, step=1)
out.em <- scanone(sug)

## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 8 individuals with missing phenotypes.

out.em2 <- scanone(sug, pheno.col=1:4)

## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 8 individuals with missing phenotypes.

## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 7 individuals with missing phenotypes.

## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 1 individuals with missing phenotypes.

## Warning in checkcovar(cross, pheno.col, addcovar, intcovar, perm.strata, : Dropping 1 individuals with missing phenotypes.

The output has “class” “scanone”. The summary function is passed to the function summary.scanone, and gives the maximum LOD score on each chromosome.

summary(out.em)

##           chr   pos   lod
## D1MIT36     1 76.73 1.449
## c2.loc77    2 82.80 1.901
## c3.loc42    3 52.82 1.393
## c4.loc43    4 47.23 0.795
## D5MIT223    5 86.57 1.312
## c6.loc26    6 27.81 0.638
## c7.loc45    7 47.71 6.109
## c8.loc34    8 54.90 1.598
## D9MIT71     9 27.07 0.769
## c10.loc51  10 60.75 0.959
## c11.loc34  11 38.70 2.157
## D12MIT145  12  2.23 1.472
## c13.loc20  13 27.26 1.119
## D14MIT138  14 12.52 1.119
## c15.loc8   15 11.96 5.257
## c16.loc31  16 45.69 0.647
## D17MIT16   17 17.98 1.241
## D18MIT22   18 13.41 1.739
## D19MIT71   19 56.28 0.402

summary(out.em2)

##           chr   pos    bp     hr     bw heart_wt
## D1MIT36     1 76.73 1.449 0.9306 0.8418  1.10701
## c2.loc77    2 82.80 1.901 3.2352 0.7491  0.14570
## c3.loc42    3 52.82 1.393 0.0199 1.7099  0.22918
## c4.loc43    4 47.23 0.795 0.9465 0.5097  0.40329
## D5MIT223    5 86.57 1.312 0.1639 1.1748  0.05581
## c6.loc26    6 27.81 0.638 0.0762 0.7033  0.15187
## c7.loc45    7 47.71 6.109 0.2060 0.5209  0.47587
## c8.loc34    8 54.90 1.598 0.3938 0.1101  1.50973
## D9MIT71     9 27.07 0.769 0.4386 0.0748  0.07482
## c10.loc51  10 60.75 0.959 0.9614 0.3983  2.60545
## c11.loc34  11 38.70 2.157 1.1985 0.5037  0.00473
## D12MIT145  12  2.23 1.472 0.3669 1.1148  0.66280
## c13.loc20  13 27.26 1.119 0.5487 0.0562  0.29918
## D14MIT138  14 12.52 1.119 0.0612 0.1672  1.39513
## c15.loc8   15 11.96 5.257 1.6771 5.6325  1.26297
## c16.loc31  16 45.69 0.647 0.4475 2.3418  0.07920
## D17MIT16   17 17.98 1.241 1.1660 0.0712  0.74646
## D18MIT22   18 13.41 1.739 0.9585 0.3425  1.39105
## D19MIT71   19 56.28 0.402 0.3015 0.0136  0.25134

Alternatively, we can give a threshold, e.g., to only see those chromosomes with LOD > 3.

summary(out.em, threshold=3)

##          chr  pos  lod
## c7.loc45   7 47.7 6.11
## c15.loc8  15 12.0 5.26

We can plot the results as follows.

plot(out.em)

plot(out.em, chr=c(7,15), ylab="LOD Score")

### iplotScanone iplotScanone creates an interactive chart with LOD curves from a genome scan linked to estimated QTL effects. If you provide just the output from scanone, the only interactivity is that hovering over marker positions on the LOD curves will give information about the marker name, position, and LOD score.

iplotScanone(out.em)

You can use the chr to plot only selected chromosomes.

iplotScanone(out.em, chr=c(7,15))

iplotScanone(out.em, sug)

iplotScanone(out.em, sug, chr=c(7,15))

Example 2: Gravitropism in Arabidopsis

We’ll consider the grav dataset included with R/qtlcharts. These are data from Moore et al. Genetics 195:1077-1086, 2013, on a QTL experiment on gravitropism in Arabidopsis, with data on 162 recombinant inbred lines (Ler × Cvi). Seedlings were sprouted and then rotated 90 degrees with respect to gravity; the growth of the seedlings was then recorded on video. The outcome is the root tip angle (in degrees) at two-minute increments over eight hours.

data(grav)

Practice

1. Get a quick summary of the grav data.

summary(grav)

##     RI strains via selfing
## 
##     No. individuals:    162 
## 
##     No. phenotypes:     241 
##     Percent phenotyped: 100 
## 
##     No. chromosomes:    5 
##         Autosomes:      1 2 3 4 5 
## 
##     Total markers:      234 
##     No. markers:        26 42 64 35 67 
##     Percent genotyped:  98.6 
##     Genotypes (%):      LL:56.5  CC:43.5

2. Make an genetic map of markers for the grav data.

plotMap(grav)

iplotMap(grav)

3. Plot a heat map of the correlation matrix for the phenotype at all pairs of times, linked to scatterplots of the phenotypes.

head(grav$pheno)

##         T0       T2       T4       T6       T8      T10      T12      T14
## 1  -3.4717  -3.2973  -3.4837  -3.8324  -3.9971  -4.4138  -4.6477  -4.9400
## 2  -3.9243  -4.1068  -4.0559  -4.1955  -4.2935  -4.5356  -4.9223  -5.2457
## 3  -7.4022  -7.4470  -7.6809  -7.7515  -8.2363  -8.7546  -9.1621  -9.9663
## 4 -11.0550 -11.0470 -11.1190 -10.8650 -10.9140 -11.2740 -11.2450 -11.2770
## 5  -5.0697  -5.1864  -5.0430  -5.4597  -5.8597  -6.3028  -7.1255  -7.4619
## 6   5.1926   5.3676   5.1018   4.8896   3.9455   3.4340   2.5305   1.8841
##        T16       T18      T20      T22      T24      T26      T28      T30
## 1  -5.4629  -5.89310  -6.5397  -6.9486  -7.7236  -8.3297  -8.9201  -9.6447
## 2  -5.5971  -5.89990  -6.1434  -6.6733  -6.9014  -7.5466  -8.0077  -8.3959
## 3 -10.2640 -10.84700 -11.8350 -12.3950 -13.1180 -13.7720 -14.6950 -15.5160
## 4 -11.2500 -11.58300 -11.6780 -11.7850 -12.1060 -12.3240 -12.7620 -13.1980
## 5  -8.3286  -8.80330  -9.7265 -10.7910 -11.7790 -12.3320 -13.4920 -14.5850
## 6   1.1394   0.12565  -0.7945  -1.5589  -2.3704  -3.5434  -4.5057  -5.5834
##        T32      T34      T36      T38     T40     T42     T44     T46     T48
## 1 -10.5440 -11.3190 -12.2730 -12.8690 -13.684 -14.672 -15.574 -16.025 -17.142
## 2  -8.7834  -9.3773  -9.8818 -10.5020 -11.141 -11.801 -12.305 -12.818 -13.592
## 3 -16.4600 -17.3470 -17.9860 -19.0310 -20.076 -20.788 -21.927 -22.833 -23.803
## 4 -13.1540 -13.4740 -14.0890 -14.4260 -14.644 -14.873 -15.340 -15.668 -15.841
## 5 -15.6780 -16.7270 -17.9550 -19.1010 -20.226 -21.375 -22.675 -23.858 -25.091
## 6  -6.6111  -7.7473  -8.7888  -9.7522 -10.943 -12.453 -13.494 -14.338 -15.433
##       T50     T52     T54     T56     T58     T60     T62     T64     T66
## 1 -17.971 -18.896 -19.540 -20.474 -21.435 -22.227 -23.179 -23.962 -24.929
## 2 -14.161 -15.022 -15.342 -16.059 -16.675 -17.457 -18.453 -19.111 -19.915
## 3 -24.647 -25.486 -26.355 -27.266 -28.387 -29.124 -29.933 -30.812 -31.520
## 4 -16.482 -16.764 -17.297 -17.689 -17.731 -18.308 -18.985 -19.643 -20.051
## 5 -26.122 -27.322 -28.556 -29.358 -30.675 -31.585 -32.698 -33.580 -34.640
## 6 -16.654 -17.801 -18.894 -19.951 -21.298 -22.422 -23.611 -24.827 -26.050
##       T68     T70     T72     T74     T76     T78     T80     T82     T84
## 1 -25.849 -26.512 -27.360 -28.071 -28.876 -29.480 -30.484 -31.342 -32.066
## 2 -20.607 -21.012 -21.775 -22.677 -23.546 -24.047 -25.123 -25.930 -26.861
## 3 -32.607 -33.320 -34.355 -35.021 -35.973 -36.892 -37.663 -38.233 -39.279
## 4 -20.590 -20.826 -21.377 -22.112 -22.785 -23.291 -23.720 -24.493 -24.903
## 5 -35.493 -36.559 -37.400 -38.325 -39.268 -40.199 -40.940 -42.132 -43.133
## 6 -27.223 -28.538 -29.651 -30.447 -31.601 -32.785 -34.192 -34.898 -36.201
##       T86     T88     T90     T92     T94     T96     T98    T100    T102
## 1 -32.975 -33.658 -34.361 -35.217 -35.801 -36.682 -37.191 -37.905 -38.623
## 2 -27.574 -28.510 -29.406 -30.092 -30.902 -31.909 -32.502 -33.525 -33.981
## 3 -40.028 -40.864 -41.413 -42.298 -42.773 -43.850 -44.697 -45.225 -45.846
## 4 -25.590 -26.123 -26.627 -27.198 -27.916 -28.560 -29.095 -29.969 -30.284
## 5 -44.022 -44.890 -45.764 -47.025 -47.880 -48.632 -49.481 -50.147 -51.120
## 6 -37.196 -38.119 -39.241 -40.157 -40.958 -41.737 -42.447 -43.291 -44.126
##      T104    T106    T108    T110    T112    T114    T116    T118    T120
## 1 -39.276 -39.972 -40.838 -41.169 -41.709 -42.570 -43.236 -43.844 -44.508
## 2 -34.874 -35.465 -36.392 -37.208 -37.845 -38.638 -39.458 -40.161 -40.790
## 3 -46.420 -47.342 -47.946 -48.515 -49.104 -49.841 -50.631 -51.493 -51.674
## 4 -30.875 -31.379 -31.936 -32.860 -33.621 -34.162 -34.754 -35.579 -36.356
## 5 -52.096 -52.939 -53.586 -54.542 -55.245 -56.242 -56.697 -57.401 -58.114
## 6 -44.981 -45.666 -46.292 -46.933 -47.495 -48.041 -48.875 -49.195 -49.590
##      T122    T124    T126    T128    T130    T132    T134    T136    T138
## 1 -45.052 -45.742 -46.536 -47.143 -47.731 -48.357 -48.989 -49.582 -50.113
## 2 -41.386 -41.937 -42.846 -43.601 -44.303 -44.793 -45.647 -46.480 -46.976
## 3 -52.500 -53.008 -53.776 -54.210 -55.042 -55.609 -56.057 -56.817 -57.477
## 4 -36.638 -37.451 -38.116 -38.886 -39.710 -40.586 -40.961 -41.802 -42.623
## 5 -58.944 -59.784 -60.385 -60.950 -61.756 -62.497 -63.080 -63.700 -64.356
## 6 -50.020 -51.037 -51.472 -51.771 -52.637 -53.307 -53.614 -54.106 -55.052
##      T140    T142    T144    T146    T148    T150    T152    T154    T156
## 1 -50.738 -51.421 -52.148 -52.767 -53.312 -53.971 -54.504 -54.942 -55.626
## 2 -47.754 -48.466 -49.081 -49.700 -50.411 -51.143 -51.633 -52.219 -52.914
## 3 -58.019 -58.512 -59.319 -60.087 -60.592 -61.258 -62.023 -62.501 -63.018
## 4 -43.263 -44.073 -44.705 -45.405 -46.227 -46.777 -47.665 -48.273 -48.942
## 5 -65.014 -65.455 -66.229 -66.747 -67.478 -67.972 -68.777 -69.356 -69.908
## 6 -55.645 -56.311 -56.876 -57.440 -57.880 -58.532 -59.336 -59.592 -59.760
##      T158    T160    T162    T164    T166    T168    T170    T172    T174
## 1 -56.166 -56.842 -57.229 -57.744 -58.272 -58.910 -59.321 -59.720 -60.315
## 2 -53.578 -54.335 -54.647 -55.308 -56.069 -56.775 -57.100 -57.883 -58.678
## 3 -63.808 -64.185 -64.809 -65.401 -65.899 -66.322 -66.832 -67.566 -68.177
## 4 -49.860 -50.554 -51.010 -52.018 -52.724 -53.145 -53.799 -54.451 -55.146
## 5 -70.409 -71.122 -71.708 -72.204 -72.922 -73.402 -73.978 -74.665 -75.099
## 6 -60.479 -61.198 -61.477 -62.208 -62.526 -63.198 -63.795 -64.299 -64.718
##      T176    T178    T180    T182    T184    T186    T188    T190    T192
## 1 -60.667 -61.412 -61.784 -62.301 -62.698 -63.181 -63.630 -64.243 -64.534
## 2 -59.000 -59.640 -60.296 -60.896 -61.238 -61.848 -62.529 -62.856 -63.434
## 3 -68.624 -69.035 -69.486 -69.937 -70.426 -70.882 -71.364 -71.729 -72.124
## 4 -56.160 -56.701 -57.650 -58.093 -58.820 -59.786 -59.997 -60.955 -61.331
## 5 -75.632 -76.167 -76.740 -77.351 -77.759 -78.294 -78.932 -79.457 -79.998
## 6 -65.145 -65.779 -66.081 -66.778 -67.294 -67.950 -68.451 -68.720 -68.932
##      T194    T196    T198    T200    T202    T204    T206    T208    T210
## 1 -64.890 -65.510 -66.168 -66.566 -66.998 -67.456 -67.958 -68.519 -69.025
## 2 -63.972 -64.391 -64.868 -65.522 -65.781 -66.487 -66.938 -67.413 -67.899
## 3 -72.640 -73.153 -73.494 -73.927 -74.286 -74.804 -75.422 -75.865 -75.938
## 4 -62.151 -62.819 -63.376 -63.922 -64.867 -65.260 -65.847 -66.765 -67.227
## 5 -80.397 -80.876 -81.357 -81.888 -82.368 -82.940 -83.207 -83.678 -84.140
## 6 -69.186 -69.873 -70.079 -70.873 -71.243 -71.757 -72.046 -72.763 -73.357
##      T212    T214    T216    T218    T220    T222    T224    T226    T228
## 1 -69.525 -69.888 -70.340 -70.804 -71.071 -71.628 -71.982 -72.534 -72.884
## 2 -68.506 -68.917 -69.466 -69.803 -70.341 -70.645 -71.224 -71.558 -72.071
## 3 -76.868 -77.163 -77.641 -78.014 -78.117 -78.610 -78.825 -79.463 -79.601
## 4 -67.731 -68.571 -69.307 -69.675 -70.307 -70.896 -71.366 -72.210 -72.740
## 5 -84.789 -85.156 -85.362 -85.674 -86.207 -86.811 -87.143 -87.494 -87.886
## 6 -74.028 -74.683 -75.128 -75.417 -75.609 -76.169 -76.466 -76.502 -76.959
##      T230    T232    T234    T236    T238    T240    T242    T244    T246
## 1 -73.329 -73.717 -74.223 -74.507 -74.996 -75.494 -75.904 -76.131 -76.718
## 2 -72.568 -73.052 -73.485 -74.223 -74.447 -74.597 -75.212 -75.660 -76.156
## 3 -79.812 -80.096 -80.464 -80.789 -80.941 -81.391 -81.544 -81.752 -82.099
## 4 -73.236 -73.972 -74.239 -74.816 -75.458 -76.175 -76.523 -76.996 -77.571
## 5 -88.418 -88.701 -89.255 -89.735 -90.126 -90.510 -90.915 -91.455 -91.664
## 6 -77.377 -77.588 -78.068 -77.785 -78.312 -78.332 -78.877 -79.312 -79.207
##      T248    T250    T252    T254    T256    T258    T260    T262    T264
## 1 -76.914 -77.289 -77.691 -77.903 -78.405 -78.835 -79.148 -79.571 -79.897
## 2 -76.597 -76.938 -77.248 -77.659 -77.926 -78.193 -78.633 -79.153 -79.166
## 3 -82.477 -82.717 -82.892 -83.298 -83.495 -83.671 -83.969 -84.261 -84.679
## 4 -77.896 -78.290 -78.925 -79.255 -79.696 -80.135 -80.729 -81.152 -81.865
## 5 -91.969 -92.461 -92.869 -93.271 -93.527 -93.960 -94.234 -94.495 -94.789
## 6 -79.655 -79.957 -80.278 -81.010 -81.135 -81.441 -81.601 -81.921 -82.378
##      T266    T268    T270    T272    T274    T276    T278    T280    T282
## 1 -80.146 -80.465 -80.836 -81.189 -81.380 -81.813 -82.299 -82.509 -82.760
## 2 -79.639 -79.781 -80.089 -80.429 -80.590 -80.940 -81.026 -81.324 -81.550
## 3 -84.798 -85.107 -85.661 -85.590 -85.700 -86.107 -86.165 -86.559 -86.784
## 4 -82.150 -82.662 -82.905 -83.314 -83.757 -84.402 -84.452 -85.065 -85.416
## 5 -94.885 -95.173 -95.431 -95.605 -95.727 -95.988 -96.357 -96.489 -96.921
## 6 -82.489 -83.009 -83.161 -83.831 -84.098 -84.380 -84.777 -84.856 -85.414
##      T284    T286    T288    T290    T292    T294    T296    T298    T300
## 1 -83.180 -83.445 -83.913 -84.118 -84.649 -85.063 -85.222 -85.364 -85.635
## 2 -82.032 -82.003 -82.412 -82.611 -82.932 -83.301 -83.387 -83.755 -83.598
## 3 -87.085 -87.311 -87.447 -87.487 -87.801 -87.793 -88.179 -88.526 -88.714
## 4 -86.040 -86.201 -86.772 -86.996 -87.480 -88.046 -88.318 -88.672 -88.791
## 5 -97.116 -97.520 -97.603 -98.002 -98.434 -98.590 -98.893 -99.189 -99.455
## 6 -85.559 -85.967 -86.472 -86.579 -87.178 -87.485 -87.809 -88.308 -88.428
##      T302    T304     T306     T308     T310     T312     T314     T316
## 1 -86.045 -86.379  -86.819  -87.009  -87.243  -87.362  -87.616  -87.946
## 2 -83.973 -84.307  -84.405  -84.524  -84.784  -85.054  -85.134  -85.136
## 3 -88.847 -89.039  -89.312  -89.584  -89.593  -89.876  -89.839  -90.507
## 4 -89.367 -89.635  -90.028  -90.664  -91.122  -91.308  -91.564  -91.952
## 5 -99.609 -99.937 -100.100 -100.320 -100.470 -100.700 -100.750 -101.080
## 6 -89.134 -89.392  -89.840  -89.814  -90.356  -90.458  -90.625  -90.910
##       T318     T320     T322     T324     T326     T328     T330     T332
## 1  -87.977  -88.284  -88.484  -88.772  -89.051  -89.202  -89.261  -89.525
## 2  -85.439  -85.563  -85.709  -85.879  -85.992  -86.095  -86.149  -86.448
## 3  -90.597  -90.877  -91.031  -91.109  -91.273  -91.450  -91.839  -91.979
## 4  -92.240  -92.625  -93.130  -93.292  -93.777  -94.309  -94.519  -94.846
## 5 -101.160 -101.520 -101.570 -101.770 -102.010 -102.170 -102.420 -102.390
## 6  -91.173  -91.513  -91.957  -92.334  -92.822  -92.643  -93.127  -93.484
##       T334     T336     T338     T340     T342     T344     T346     T348
## 1  -89.723  -89.862  -90.026  -90.355  -90.443  -90.687  -90.730  -90.978
## 2  -86.536  -86.451  -86.695  -86.751  -86.783  -87.060  -87.229  -87.124
## 3  -91.940  -92.289  -92.713  -92.803  -92.932  -93.384  -93.329  -93.556
## 4  -95.025  -95.227  -95.798  -96.112  -96.386  -96.618  -96.919  -97.099
## 5 -102.840 -102.870 -102.870 -103.090 -103.260 -103.430 -103.570 -103.750
## 6  -93.702  -94.205  -94.332  -94.394  -94.628  -94.787  -94.946  -95.193
##       T350     T352     T354     T356     T358     T360     T362     T364
## 1  -91.206  -91.419  -91.394  -91.735  -91.794  -92.140  -92.236  -92.308
## 2  -87.348  -87.360  -87.539  -87.628  -87.816  -87.621  -87.898  -87.725
## 3  -93.822  -94.122  -94.282  -94.682  -94.701  -94.720  -95.087  -95.412
## 4  -97.614  -97.838  -98.052  -98.174  -98.631  -98.987  -99.055  -99.343
## 5 -103.960 -103.990 -104.030 -104.300 -104.270 -104.310 -104.520 -104.590
## 6  -95.493  -95.652  -96.038  -95.958  -96.167  -96.210  -96.371  -96.819
##       T366     T368     T370     T372     T374     T376     T378     T380
## 1  -92.396  -92.472  -92.614  -92.715  -92.981  -93.106  -93.136  -93.330
## 2  -87.956  -88.114  -88.235  -88.117  -88.404  -88.450  -88.395  -88.550
## 3  -95.599  -95.679  -96.030  -96.170  -96.307  -96.638  -96.728  -96.900
## 4  -99.689  -99.857 -100.020 -100.370 -100.440 -100.620 -101.000 -101.140
## 5 -104.860 -104.970 -105.110 -105.160 -105.220 -105.510 -105.380 -105.600
## 6  -96.846  -96.926  -97.020  -97.233  -97.629  -97.811  -97.596  -97.767
##       T382     T384     T386     T388     T390     T392     T394     T396
## 1  -93.535  -93.622  -93.467  -93.637  -93.701  -94.044  -93.921  -94.127
## 2  -88.894  -88.800  -88.888  -88.917  -89.206  -89.119  -89.149  -89.436
## 3  -97.169  -97.462  -97.303  -97.457  -97.607  -97.979  -97.960  -98.244
## 4 -101.340 -101.440 -101.960 -101.830 -102.320 -102.420 -102.220 -102.570
## 5 -105.670 -105.640 -105.770 -105.940 -105.970 -105.950 -106.110 -106.360
## 6  -98.186  -98.230  -98.299  -98.318  -98.578  -98.589  -98.901  -98.785
##       T398     T400     T402     T404     T406     T408     T410     T412
## 1  -94.271  -94.311  -94.284  -94.401  -94.329  -94.258  -94.405  -94.586
## 2  -89.402  -89.636  -89.544  -89.537  -89.522  -89.620  -90.011  -89.965
## 3  -98.601  -98.688  -99.255  -99.294  -99.414  -99.634  -99.706 -100.050
## 4 -102.830 -102.800 -103.140 -103.250 -103.650 -103.670 -103.580 -104.040
## 5 -106.200 -106.400 -106.500 -106.510 -106.700 -106.610 -106.690 -106.770
## 6  -99.164  -99.236  -99.338  -99.498  -99.631  -99.851  -99.913  -99.750
##       T414     T416     T418     T420     T422     T424     T426     T428
## 1  -94.574  -94.559  -94.931  -95.030  -95.100  -95.002  -94.916  -94.937
## 2  -89.897  -89.821  -89.972  -90.071  -90.138  -90.228  -90.139  -90.353
## 3 -100.400 -100.280 -100.450 -100.500 -100.720 -100.850 -101.270 -101.170
## 4 -103.890 -103.770 -104.010 -104.140 -103.970 -104.240 -104.180 -104.350
## 5 -106.650 -106.960 -106.920 -107.010 -107.120 -107.200 -107.300 -107.270
## 6 -100.110 -100.060 -100.210 -100.300 -100.720 -100.520 -100.810 -100.310
##       T430     T432     T434     T436     T438     T440     T442     T444
## 1  -94.944  -95.006  -95.079  -95.109  -95.011  -95.119  -95.075  -95.137
## 2  -90.601  -90.554  -90.881  -90.826  -90.654  -90.717  -90.799  -90.930
## 3 -101.270 -101.490 -101.500 -101.730 -101.720 -101.890 -102.020 -102.020
## 4 -104.410 -104.600 -104.530 -104.690 -104.640 -104.700 -105.020 -104.690
## 5 -107.220 -107.450 -107.550 -107.580 -107.400 -107.470 -107.580 -107.590
## 6 -100.770 -100.690 -100.940 -100.570 -100.700 -101.000 -100.980 -101.180
##       T446     T448     T450     T452     T454     T456     T458     T460
## 1  -95.205  -95.238  -95.257  -95.304  -95.402  -95.519  -95.376  -95.449
## 2  -90.842  -91.178  -91.287  -91.265  -91.464  -91.657  -91.600  -91.697
## 3 -102.120 -102.290 -102.360 -102.590 -102.850 -102.940 -102.800 -102.840
## 4 -104.720 -104.770 -104.810 -104.640 -104.650 -104.940 -104.800 -104.610
## 5 -107.650 -107.720 -107.670 -107.580 -107.600 -107.720 -107.680 -107.790
## 6 -101.120 -101.250 -101.330 -101.310 -101.360 -101.220 -101.390 -101.370
##       T462     T464     T466     T468     T470     T472     T474     T476
## 1  -95.503  -95.568  -95.570  -95.554  -95.740  -95.859  -95.846  -95.810
## 2  -91.697  -92.019  -91.974  -92.120  -92.209  -92.246  -92.432  -92.452
## 3 -103.060 -103.270 -103.300 -103.510 -103.600 -103.810 -103.930 -103.720
## 4 -104.600 -104.550 -104.530 -104.530 -104.360 -104.440 -104.290 -104.410
## 5 -107.770 -107.890 -108.020 -108.170 -108.340 -108.310 -108.420 -108.490
## 6 -101.360 -101.380 -101.460 -101.580 -101.650 -101.660 -101.850 -101.970
##       T478     T480
## 1  -95.985  -95.967
## 2  -92.303  -92.495
## 3 -103.860 -104.000
## 4 -104.280 -104.210
## 5 -108.380 -108.450
## 6 -102.070 -102.130

iplotCorr(grav$pheno)

iplotCurves

iplotCurves creates a plot of a set of curves linked to one or two scatterplots.

iplotCurves(grav$pheno)

times1 <- as.numeric(sub("T", "", phenames(grav)))/60
times1

##   [1] 0.00000000 0.03333333 0.06666667 0.10000000 0.13333333 0.16666667
##   [7] 0.20000000 0.23333333 0.26666667 0.30000000 0.33333333 0.36666667
##  [13] 0.40000000 0.43333333 0.46666667 0.50000000 0.53333333 0.56666667
##  [19] 0.60000000 0.63333333 0.66666667 0.70000000 0.73333333 0.76666667
##  [25] 0.80000000 0.83333333 0.86666667 0.90000000 0.93333333 0.96666667
##  [31] 1.00000000 1.03333333 1.06666667 1.10000000 1.13333333 1.16666667
##  [37] 1.20000000 1.23333333 1.26666667 1.30000000 1.33333333 1.36666667
##  [43] 1.40000000 1.43333333 1.46666667 1.50000000 1.53333333 1.56666667
##  [49] 1.60000000 1.63333333 1.66666667 1.70000000 1.73333333 1.76666667
##  [55] 1.80000000 1.83333333 1.86666667 1.90000000 1.93333333 1.96666667
##  [61] 2.00000000 2.03333333 2.06666667 2.10000000 2.13333333 2.16666667
##  [67] 2.20000000 2.23333333 2.26666667 2.30000000 2.33333333 2.36666667
##  [73] 2.40000000 2.43333333 2.46666667 2.50000000 2.53333333 2.56666667
##  [79] 2.60000000 2.63333333 2.66666667 2.70000000 2.73333333 2.76666667
##  [85] 2.80000000 2.83333333 2.86666667 2.90000000 2.93333333 2.96666667
##  [91] 3.00000000 3.03333333 3.06666667 3.10000000 3.13333333 3.16666667
##  [97] 3.20000000 3.23333333 3.26666667 3.30000000 3.33333333 3.36666667
## [103] 3.40000000 3.43333333 3.46666667 3.50000000 3.53333333 3.56666667
## [109] 3.60000000 3.63333333 3.66666667 3.70000000 3.73333333 3.76666667
## [115] 3.80000000 3.83333333 3.86666667 3.90000000 3.93333333 3.96666667
## [121] 4.00000000 4.03333333 4.06666667 4.10000000 4.13333333 4.16666667
## [127] 4.20000000 4.23333333 4.26666667 4.30000000 4.33333333 4.36666667
## [133] 4.40000000 4.43333333 4.46666667 4.50000000 4.53333333 4.56666667
## [139] 4.60000000 4.63333333 4.66666667 4.70000000 4.73333333 4.76666667
## [145] 4.80000000 4.83333333 4.86666667 4.90000000 4.93333333 4.96666667
## [151] 5.00000000 5.03333333 5.06666667 5.10000000 5.13333333 5.16666667
## [157] 5.20000000 5.23333333 5.26666667 5.30000000 5.33333333 5.36666667
## [163] 5.40000000 5.43333333 5.46666667 5.50000000 5.53333333 5.56666667
## [169] 5.60000000 5.63333333 5.66666667 5.70000000 5.73333333 5.76666667
## [175] 5.80000000 5.83333333 5.86666667 5.90000000 5.93333333 5.96666667
## [181] 6.00000000 6.03333333 6.06666667 6.10000000 6.13333333 6.16666667
## [187] 6.20000000 6.23333333 6.26666667 6.30000000 6.33333333 6.36666667
## [193] 6.40000000 6.43333333 6.46666667 6.50000000 6.53333333 6.56666667
## [199] 6.60000000 6.63333333 6.66666667 6.70000000 6.73333333 6.76666667
## [205] 6.80000000 6.83333333 6.86666667 6.90000000 6.93333333 6.96666667
## [211] 7.00000000 7.03333333 7.06666667 7.10000000 7.13333333 7.16666667
## [217] 7.20000000 7.23333333 7.26666667 7.30000000 7.33333333 7.36666667
## [223] 7.40000000 7.43333333 7.46666667 7.50000000 7.53333333 7.56666667
## [229] 7.60000000 7.63333333 7.66666667 7.70000000 7.73333333 7.76666667
## [235] 7.80000000 7.83333333 7.86666667 7.90000000 7.93333333 7.96666667
## [241] 8.00000000

times <- attr(grav, "time")
times

##   [1] 0.00000000 0.03333333 0.06666667 0.10000000 0.13333333 0.16666667
##   [7] 0.20000000 0.23333333 0.26666667 0.30000000 0.33333333 0.36666667
##  [13] 0.40000000 0.43333333 0.46666667 0.50000000 0.53333333 0.56666667
##  [19] 0.60000000 0.63333333 0.66666667 0.70000000 0.73333333 0.76666667
##  [25] 0.80000000 0.83333333 0.86666667 0.90000000 0.93333333 0.96666667
##  [31] 1.00000000 1.03333333 1.06666667 1.10000000 1.13333333 1.16666667
##  [37] 1.20000000 1.23333333 1.26666667 1.30000000 1.33333333 1.36666667
##  [43] 1.40000000 1.43333333 1.46666667 1.50000000 1.53333333 1.56666667
##  [49] 1.60000000 1.63333333 1.66666667 1.70000000 1.73333333 1.76666667
##  [55] 1.80000000 1.83333333 1.86666667 1.90000000 1.93333333 1.96666667
##  [61] 2.00000000 2.03333333 2.06666667 2.10000000 2.13333333 2.16666667
##  [67] 2.20000000 2.23333333 2.26666667 2.30000000 2.33333333 2.36666667
##  [73] 2.40000000 2.43333333 2.46666667 2.50000000 2.53333333 2.56666667
##  [79] 2.60000000 2.63333333 2.66666667 2.70000000 2.73333333 2.76666667
##  [85] 2.80000000 2.83333333 2.86666667 2.90000000 2.93333333 2.96666667
##  [91] 3.00000000 3.03333333 3.06666667 3.10000000 3.13333333 3.16666667
##  [97] 3.20000000 3.23333333 3.26666667 3.30000000 3.33333333 3.36666667
## [103] 3.40000000 3.43333333 3.46666667 3.50000000 3.53333333 3.56666667
## [109] 3.60000000 3.63333333 3.66666667 3.70000000 3.73333333 3.76666667
## [115] 3.80000000 3.83333333 3.86666667 3.90000000 3.93333333 3.96666667
## [121] 4.00000000 4.03333333 4.06666667 4.10000000 4.13333333 4.16666667
## [127] 4.20000000 4.23333333 4.26666667 4.30000000 4.33333333 4.36666667
## [133] 4.40000000 4.43333333 4.46666667 4.50000000 4.53333333 4.56666667
## [139] 4.60000000 4.63333333 4.66666667 4.70000000 4.73333333 4.76666667
## [145] 4.80000000 4.83333333 4.86666667 4.90000000 4.93333333 4.96666667
## [151] 5.00000000 5.03333333 5.06666667 5.10000000 5.13333333 5.16666667
## [157] 5.20000000 5.23333333 5.26666667 5.30000000 5.33333333 5.36666667
## [163] 5.40000000 5.43333333 5.46666667 5.50000000 5.53333333 5.56666667
## [169] 5.60000000 5.63333333 5.66666667 5.70000000 5.73333333 5.76666667
## [175] 5.80000000 5.83333333 5.86666667 5.90000000 5.93333333 5.96666667
## [181] 6.00000000 6.03333333 6.06666667 6.10000000 6.13333333 6.16666667
## [187] 6.20000000 6.23333333 6.26666667 6.30000000 6.33333333 6.36666667
## [193] 6.40000000 6.43333333 6.46666667 6.50000000 6.53333333 6.56666667
## [199] 6.60000000 6.63333333 6.66666667 6.70000000 6.73333333 6.76666667
## [205] 6.80000000 6.83333333 6.86666667 6.90000000 6.93333333 6.96666667
## [211] 7.00000000 7.03333333 7.06666667 7.10000000 7.13333333 7.16666667
## [217] 7.20000000 7.23333333 7.26666667 7.30000000 7.33333333 7.36666667
## [223] 7.40000000 7.43333333 7.46666667 7.50000000 7.53333333 7.56666667
## [229] 7.60000000 7.63333333 7.66666667 7.70000000 7.73333333 7.76666667
## [235] 7.80000000 7.83333333 7.86666667 7.90000000 7.93333333 7.96666667
## [241] 8.00000000

#?attr
phe <- grav$pheno
head(phe)

##         T0       T2       T4       T6       T8      T10      T12      T14
## 1  -3.4717  -3.2973  -3.4837  -3.8324  -3.9971  -4.4138  -4.6477  -4.9400
## 2  -3.9243  -4.1068  -4.0559  -4.1955  -4.2935  -4.5356  -4.9223  -5.2457
## 3  -7.4022  -7.4470  -7.6809  -7.7515  -8.2363  -8.7546  -9.1621  -9.9663
## 4 -11.0550 -11.0470 -11.1190 -10.8650 -10.9140 -11.2740 -11.2450 -11.2770
## 5  -5.0697  -5.1864  -5.0430  -5.4597  -5.8597  -6.3028  -7.1255  -7.4619
## 6   5.1926   5.3676   5.1018   4.8896   3.9455   3.4340   2.5305   1.8841
##        T16       T18      T20      T22      T24      T26      T28      T30
## 1  -5.4629  -5.89310  -6.5397  -6.9486  -7.7236  -8.3297  -8.9201  -9.6447
## 2  -5.5971  -5.89990  -6.1434  -6.6733  -6.9014  -7.5466  -8.0077  -8.3959
## 3 -10.2640 -10.84700 -11.8350 -12.3950 -13.1180 -13.7720 -14.6950 -15.5160
## 4 -11.2500 -11.58300 -11.6780 -11.7850 -12.1060 -12.3240 -12.7620 -13.1980
## 5  -8.3286  -8.80330  -9.7265 -10.7910 -11.7790 -12.3320 -13.4920 -14.5850
## 6   1.1394   0.12565  -0.7945  -1.5589  -2.3704  -3.5434  -4.5057  -5.5834
##        T32      T34      T36      T38     T40     T42     T44     T46     T48
## 1 -10.5440 -11.3190 -12.2730 -12.8690 -13.684 -14.672 -15.574 -16.025 -17.142
## 2  -8.7834  -9.3773  -9.8818 -10.5020 -11.141 -11.801 -12.305 -12.818 -13.592
## 3 -16.4600 -17.3470 -17.9860 -19.0310 -20.076 -20.788 -21.927 -22.833 -23.803
## 4 -13.1540 -13.4740 -14.0890 -14.4260 -14.644 -14.873 -15.340 -15.668 -15.841
## 5 -15.6780 -16.7270 -17.9550 -19.1010 -20.226 -21.375 -22.675 -23.858 -25.091
## 6  -6.6111  -7.7473  -8.7888  -9.7522 -10.943 -12.453 -13.494 -14.338 -15.433
##       T50     T52     T54     T56     T58     T60     T62     T64     T66
## 1 -17.971 -18.896 -19.540 -20.474 -21.435 -22.227 -23.179 -23.962 -24.929
## 2 -14.161 -15.022 -15.342 -16.059 -16.675 -17.457 -18.453 -19.111 -19.915
## 3 -24.647 -25.486 -26.355 -27.266 -28.387 -29.124 -29.933 -30.812 -31.520
## 4 -16.482 -16.764 -17.297 -17.689 -17.731 -18.308 -18.985 -19.643 -20.051
## 5 -26.122 -27.322 -28.556 -29.358 -30.675 -31.585 -32.698 -33.580 -34.640
## 6 -16.654 -17.801 -18.894 -19.951 -21.298 -22.422 -23.611 -24.827 -26.050
##       T68     T70     T72     T74     T76     T78     T80     T82     T84
## 1 -25.849 -26.512 -27.360 -28.071 -28.876 -29.480 -30.484 -31.342 -32.066
## 2 -20.607 -21.012 -21.775 -22.677 -23.546 -24.047 -25.123 -25.930 -26.861
## 3 -32.607 -33.320 -34.355 -35.021 -35.973 -36.892 -37.663 -38.233 -39.279
## 4 -20.590 -20.826 -21.377 -22.112 -22.785 -23.291 -23.720 -24.493 -24.903
## 5 -35.493 -36.559 -37.400 -38.325 -39.268 -40.199 -40.940 -42.132 -43.133
## 6 -27.223 -28.538 -29.651 -30.447 -31.601 -32.785 -34.192 -34.898 -36.201
##       T86     T88     T90     T92     T94     T96     T98    T100    T102
## 1 -32.975 -33.658 -34.361 -35.217 -35.801 -36.682 -37.191 -37.905 -38.623
## 2 -27.574 -28.510 -29.406 -30.092 -30.902 -31.909 -32.502 -33.525 -33.981
## 3 -40.028 -40.864 -41.413 -42.298 -42.773 -43.850 -44.697 -45.225 -45.846
## 4 -25.590 -26.123 -26.627 -27.198 -27.916 -28.560 -29.095 -29.969 -30.284
## 5 -44.022 -44.890 -45.764 -47.025 -47.880 -48.632 -49.481 -50.147 -51.120
## 6 -37.196 -38.119 -39.241 -40.157 -40.958 -41.737 -42.447 -43.291 -44.126
##      T104    T106    T108    T110    T112    T114    T116    T118    T120
## 1 -39.276 -39.972 -40.838 -41.169 -41.709 -42.570 -43.236 -43.844 -44.508
## 2 -34.874 -35.465 -36.392 -37.208 -37.845 -38.638 -39.458 -40.161 -40.790
## 3 -46.420 -47.342 -47.946 -48.515 -49.104 -49.841 -50.631 -51.493 -51.674
## 4 -30.875 -31.379 -31.936 -32.860 -33.621 -34.162 -34.754 -35.579 -36.356
## 5 -52.096 -52.939 -53.586 -54.542 -55.245 -56.242 -56.697 -57.401 -58.114
## 6 -44.981 -45.666 -46.292 -46.933 -47.495 -48.041 -48.875 -49.195 -49.590
##      T122    T124    T126    T128    T130    T132    T134    T136    T138
## 1 -45.052 -45.742 -46.536 -47.143 -47.731 -48.357 -48.989 -49.582 -50.113
## 2 -41.386 -41.937 -42.846 -43.601 -44.303 -44.793 -45.647 -46.480 -46.976
## 3 -52.500 -53.008 -53.776 -54.210 -55.042 -55.609 -56.057 -56.817 -57.477
## 4 -36.638 -37.451 -38.116 -38.886 -39.710 -40.586 -40.961 -41.802 -42.623
## 5 -58.944 -59.784 -60.385 -60.950 -61.756 -62.497 -63.080 -63.700 -64.356
## 6 -50.020 -51.037 -51.472 -51.771 -52.637 -53.307 -53.614 -54.106 -55.052
##      T140    T142    T144    T146    T148    T150    T152    T154    T156
## 1 -50.738 -51.421 -52.148 -52.767 -53.312 -53.971 -54.504 -54.942 -55.626
## 2 -47.754 -48.466 -49.081 -49.700 -50.411 -51.143 -51.633 -52.219 -52.914
## 3 -58.019 -58.512 -59.319 -60.087 -60.592 -61.258 -62.023 -62.501 -63.018
## 4 -43.263 -44.073 -44.705 -45.405 -46.227 -46.777 -47.665 -48.273 -48.942
## 5 -65.014 -65.455 -66.229 -66.747 -67.478 -67.972 -68.777 -69.356 -69.908
## 6 -55.645 -56.311 -56.876 -57.440 -57.880 -58.532 -59.336 -59.592 -59.760
##      T158    T160    T162    T164    T166    T168    T170    T172    T174
## 1 -56.166 -56.842 -57.229 -57.744 -58.272 -58.910 -59.321 -59.720 -60.315
## 2 -53.578 -54.335 -54.647 -55.308 -56.069 -56.775 -57.100 -57.883 -58.678
## 3 -63.808 -64.185 -64.809 -65.401 -65.899 -66.322 -66.832 -67.566 -68.177
## 4 -49.860 -50.554 -51.010 -52.018 -52.724 -53.145 -53.799 -54.451 -55.146
## 5 -70.409 -71.122 -71.708 -72.204 -72.922 -73.402 -73.978 -74.665 -75.099
## 6 -60.479 -61.198 -61.477 -62.208 -62.526 -63.198 -63.795 -64.299 -64.718
##      T176    T178    T180    T182    T184    T186    T188    T190    T192
## 1 -60.667 -61.412 -61.784 -62.301 -62.698 -63.181 -63.630 -64.243 -64.534
## 2 -59.000 -59.640 -60.296 -60.896 -61.238 -61.848 -62.529 -62.856 -63.434
## 3 -68.624 -69.035 -69.486 -69.937 -70.426 -70.882 -71.364 -71.729 -72.124
## 4 -56.160 -56.701 -57.650 -58.093 -58.820 -59.786 -59.997 -60.955 -61.331
## 5 -75.632 -76.167 -76.740 -77.351 -77.759 -78.294 -78.932 -79.457 -79.998
## 6 -65.145 -65.779 -66.081 -66.778 -67.294 -67.950 -68.451 -68.720 -68.932
##      T194    T196    T198    T200    T202    T204    T206    T208    T210
## 1 -64.890 -65.510 -66.168 -66.566 -66.998 -67.456 -67.958 -68.519 -69.025
## 2 -63.972 -64.391 -64.868 -65.522 -65.781 -66.487 -66.938 -67.413 -67.899
## 3 -72.640 -73.153 -73.494 -73.927 -74.286 -74.804 -75.422 -75.865 -75.938
## 4 -62.151 -62.819 -63.376 -63.922 -64.867 -65.260 -65.847 -66.765 -67.227
## 5 -80.397 -80.876 -81.357 -81.888 -82.368 -82.940 -83.207 -83.678 -84.140
## 6 -69.186 -69.873 -70.079 -70.873 -71.243 -71.757 -72.046 -72.763 -73.357
##      T212    T214    T216    T218    T220    T222    T224    T226    T228
## 1 -69.525 -69.888 -70.340 -70.804 -71.071 -71.628 -71.982 -72.534 -72.884
## 2 -68.506 -68.917 -69.466 -69.803 -70.341 -70.645 -71.224 -71.558 -72.071
## 3 -76.868 -77.163 -77.641 -78.014 -78.117 -78.610 -78.825 -79.463 -79.601
## 4 -67.731 -68.571 -69.307 -69.675 -70.307 -70.896 -71.366 -72.210 -72.740
## 5 -84.789 -85.156 -85.362 -85.674 -86.207 -86.811 -87.143 -87.494 -87.886
## 6 -74.028 -74.683 -75.128 -75.417 -75.609 -76.169 -76.466 -76.502 -76.959
##      T230    T232    T234    T236    T238    T240    T242    T244    T246
## 1 -73.329 -73.717 -74.223 -74.507 -74.996 -75.494 -75.904 -76.131 -76.718
## 2 -72.568 -73.052 -73.485 -74.223 -74.447 -74.597 -75.212 -75.660 -76.156
## 3 -79.812 -80.096 -80.464 -80.789 -80.941 -81.391 -81.544 -81.752 -82.099
## 4 -73.236 -73.972 -74.239 -74.816 -75.458 -76.175 -76.523 -76.996 -77.571
## 5 -88.418 -88.701 -89.255 -89.735 -90.126 -90.510 -90.915 -91.455 -91.664
## 6 -77.377 -77.588 -78.068 -77.785 -78.312 -78.332 -78.877 -79.312 -79.207
##      T248    T250    T252    T254    T256    T258    T260    T262    T264
## 1 -76.914 -77.289 -77.691 -77.903 -78.405 -78.835 -79.148 -79.571 -79.897
## 2 -76.597 -76.938 -77.248 -77.659 -77.926 -78.193 -78.633 -79.153 -79.166
## 3 -82.477 -82.717 -82.892 -83.298 -83.495 -83.671 -83.969 -84.261 -84.679
## 4 -77.896 -78.290 -78.925 -79.255 -79.696 -80.135 -80.729 -81.152 -81.865
## 5 -91.969 -92.461 -92.869 -93.271 -93.527 -93.960 -94.234 -94.495 -94.789
## 6 -79.655 -79.957 -80.278 -81.010 -81.135 -81.441 -81.601 -81.921 -82.378
##      T266    T268    T270    T272    T274    T276    T278    T280    T282
## 1 -80.146 -80.465 -80.836 -81.189 -81.380 -81.813 -82.299 -82.509 -82.760
## 2 -79.639 -79.781 -80.089 -80.429 -80.590 -80.940 -81.026 -81.324 -81.550
## 3 -84.798 -85.107 -85.661 -85.590 -85.700 -86.107 -86.165 -86.559 -86.784
## 4 -82.150 -82.662 -82.905 -83.314 -83.757 -84.402 -84.452 -85.065 -85.416
## 5 -94.885 -95.173 -95.431 -95.605 -95.727 -95.988 -96.357 -96.489 -96.921
## 6 -82.489 -83.009 -83.161 -83.831 -84.098 -84.380 -84.777 -84.856 -85.414
##      T284    T286    T288    T290    T292    T294    T296    T298    T300
## 1 -83.180 -83.445 -83.913 -84.118 -84.649 -85.063 -85.222 -85.364 -85.635
## 2 -82.032 -82.003 -82.412 -82.611 -82.932 -83.301 -83.387 -83.755 -83.598
## 3 -87.085 -87.311 -87.447 -87.487 -87.801 -87.793 -88.179 -88.526 -88.714
## 4 -86.040 -86.201 -86.772 -86.996 -87.480 -88.046 -88.318 -88.672 -88.791
## 5 -97.116 -97.520 -97.603 -98.002 -98.434 -98.590 -98.893 -99.189 -99.455
## 6 -85.559 -85.967 -86.472 -86.579 -87.178 -87.485 -87.809 -88.308 -88.428
##      T302    T304     T306     T308     T310     T312     T314     T316
## 1 -86.045 -86.379  -86.819  -87.009  -87.243  -87.362  -87.616  -87.946
## 2 -83.973 -84.307  -84.405  -84.524  -84.784  -85.054  -85.134  -85.136
## 3 -88.847 -89.039  -89.312  -89.584  -89.593  -89.876  -89.839  -90.507
## 4 -89.367 -89.635  -90.028  -90.664  -91.122  -91.308  -91.564  -91.952
## 5 -99.609 -99.937 -100.100 -100.320 -100.470 -100.700 -100.750 -101.080
## 6 -89.134 -89.392  -89.840  -89.814  -90.356  -90.458  -90.625  -90.910
##       T318     T320     T322     T324     T326     T328     T330     T332
## 1  -87.977  -88.284  -88.484  -88.772  -89.051  -89.202  -89.261  -89.525
## 2  -85.439  -85.563  -85.709  -85.879  -85.992  -86.095  -86.149  -86.448
## 3  -90.597  -90.877  -91.031  -91.109  -91.273  -91.450  -91.839  -91.979
## 4  -92.240  -92.625  -93.130  -93.292  -93.777  -94.309  -94.519  -94.846
## 5 -101.160 -101.520 -101.570 -101.770 -102.010 -102.170 -102.420 -102.390
## 6  -91.173  -91.513  -91.957  -92.334  -92.822  -92.643  -93.127  -93.484
##       T334     T336     T338     T340     T342     T344     T346     T348
## 1  -89.723  -89.862  -90.026  -90.355  -90.443  -90.687  -90.730  -90.978
## 2  -86.536  -86.451  -86.695  -86.751  -86.783  -87.060  -87.229  -87.124
## 3  -91.940  -92.289  -92.713  -92.803  -92.932  -93.384  -93.329  -93.556
## 4  -95.025  -95.227  -95.798  -96.112  -96.386  -96.618  -96.919  -97.099
## 5 -102.840 -102.870 -102.870 -103.090 -103.260 -103.430 -103.570 -103.750
## 6  -93.702  -94.205  -94.332  -94.394  -94.628  -94.787  -94.946  -95.193
##       T350     T352     T354     T356     T358     T360     T362     T364
## 1  -91.206  -91.419  -91.394  -91.735  -91.794  -92.140  -92.236  -92.308
## 2  -87.348  -87.360  -87.539  -87.628  -87.816  -87.621  -87.898  -87.725
## 3  -93.822  -94.122  -94.282  -94.682  -94.701  -94.720  -95.087  -95.412
## 4  -97.614  -97.838  -98.052  -98.174  -98.631  -98.987  -99.055  -99.343
## 5 -103.960 -103.990 -104.030 -104.300 -104.270 -104.310 -104.520 -104.590
## 6  -95.493  -95.652  -96.038  -95.958  -96.167  -96.210  -96.371  -96.819
##       T366     T368     T370     T372     T374     T376     T378     T380
## 1  -92.396  -92.472  -92.614  -92.715  -92.981  -93.106  -93.136  -93.330
## 2  -87.956  -88.114  -88.235  -88.117  -88.404  -88.450  -88.395  -88.550
## 3  -95.599  -95.679  -96.030  -96.170  -96.307  -96.638  -96.728  -96.900
## 4  -99.689  -99.857 -100.020 -100.370 -100.440 -100.620 -101.000 -101.140
## 5 -104.860 -104.970 -105.110 -105.160 -105.220 -105.510 -105.380 -105.600
## 6  -96.846  -96.926  -97.020  -97.233  -97.629  -97.811  -97.596  -97.767
##       T382     T384     T386     T388     T390     T392     T394     T396
## 1  -93.535  -93.622  -93.467  -93.637  -93.701  -94.044  -93.921  -94.127
## 2  -88.894  -88.800  -88.888  -88.917  -89.206  -89.119  -89.149  -89.436
## 3  -97.169  -97.462  -97.303  -97.457  -97.607  -97.979  -97.960  -98.244
## 4 -101.340 -101.440 -101.960 -101.830 -102.320 -102.420 -102.220 -102.570
## 5 -105.670 -105.640 -105.770 -105.940 -105.970 -105.950 -106.110 -106.360
## 6  -98.186  -98.230  -98.299  -98.318  -98.578  -98.589  -98.901  -98.785
##       T398     T400     T402     T404     T406     T408     T410     T412
## 1  -94.271  -94.311  -94.284  -94.401  -94.329  -94.258  -94.405  -94.586
## 2  -89.402  -89.636  -89.544  -89.537  -89.522  -89.620  -90.011  -89.965
## 3  -98.601  -98.688  -99.255  -99.294  -99.414  -99.634  -99.706 -100.050
## 4 -102.830 -102.800 -103.140 -103.250 -103.650 -103.670 -103.580 -104.040
## 5 -106.200 -106.400 -106.500 -106.510 -106.700 -106.610 -106.690 -106.770
## 6  -99.164  -99.236  -99.338  -99.498  -99.631  -99.851  -99.913  -99.750
##       T414     T416     T418     T420     T422     T424     T426     T428
## 1  -94.574  -94.559  -94.931  -95.030  -95.100  -95.002  -94.916  -94.937
## 2  -89.897  -89.821  -89.972  -90.071  -90.138  -90.228  -90.139  -90.353
## 3 -100.400 -100.280 -100.450 -100.500 -100.720 -100.850 -101.270 -101.170
## 4 -103.890 -103.770 -104.010 -104.140 -103.970 -104.240 -104.180 -104.350
## 5 -106.650 -106.960 -106.920 -107.010 -107.120 -107.200 -107.300 -107.270
## 6 -100.110 -100.060 -100.210 -100.300 -100.720 -100.520 -100.810 -100.310
##       T430     T432     T434     T436     T438     T440     T442     T444
## 1  -94.944  -95.006  -95.079  -95.109  -95.011  -95.119  -95.075  -95.137
## 2  -90.601  -90.554  -90.881  -90.826  -90.654  -90.717  -90.799  -90.930
## 3 -101.270 -101.490 -101.500 -101.730 -101.720 -101.890 -102.020 -102.020
## 4 -104.410 -104.600 -104.530 -104.690 -104.640 -104.700 -105.020 -104.690
## 5 -107.220 -107.450 -107.550 -107.580 -107.400 -107.470 -107.580 -107.590
## 6 -100.770 -100.690 -100.940 -100.570 -100.700 -101.000 -100.980 -101.180
##       T446     T448     T450     T452     T454     T456     T458     T460
## 1  -95.205  -95.238  -95.257  -95.304  -95.402  -95.519  -95.376  -95.449
## 2  -90.842  -91.178  -91.287  -91.265  -91.464  -91.657  -91.600  -91.697
## 3 -102.120 -102.290 -102.360 -102.590 -102.850 -102.940 -102.800 -102.840
## 4 -104.720 -104.770 -104.810 -104.640 -104.650 -104.940 -104.800 -104.610
## 5 -107.650 -107.720 -107.670 -107.580 -107.600 -107.720 -107.680 -107.790
## 6 -101.120 -101.250 -101.330 -101.310 -101.360 -101.220 -101.390 -101.370
##       T462     T464     T466     T468     T470     T472     T474     T476
## 1  -95.503  -95.568  -95.570  -95.554  -95.740  -95.859  -95.846  -95.810
## 2  -91.697  -92.019  -91.974  -92.120  -92.209  -92.246  -92.432  -92.452
## 3 -103.060 -103.270 -103.300 -103.510 -103.600 -103.810 -103.930 -103.720
## 4 -104.600 -104.550 -104.530 -104.530 -104.360 -104.440 -104.290 -104.410
## 5 -107.770 -107.890 -108.020 -108.170 -108.340 -108.310 -108.420 -108.490
## 6 -101.360 -101.380 -101.460 -101.580 -101.650 -101.660 -101.850 -101.970
##       T478     T480
## 1  -95.985  -95.967
## 2  -92.303  -92.495
## 3 -103.860 -104.000
## 4 -104.280 -104.210
## 5 -108.380 -108.450
## 6 -102.070 -102.130

iplotCurves(phe, times)

To change the axis labels, pass curves_xlab and curves_ylab using chartOpts.

iplotCurves(phe, times, chartOpts=list(curves_xlab="Time (hrs)",
                                       curves_ylab="Response"))

iplotCurves(phe, times, phe[,c("T30", "T240")], phe[,c("T240", "T480")],
            chartOpts=list(curves_xlab="Time", curves_ylab="Root tip angle",
                           scat1_xlab="Angle at 30 min", scat1_ylab="Angle at 4 hrs",
                           scat2_xlab="Angle at 4 hrs", scat2_ylab="Angle at 8 hrs"))

Let’s further run a single-QTL genome scan with each individual time point.

grav <- calc.genoprob(grav, step=1)
out.em <- scanone(grav, pheno.col=1:nphe(grav))
summary(out.em)

##                chr  pos    T0    T2    T4    T6    T8   T10   T12   T14   T16
## c1.loc44         1 44.0 1.780 1.822 1.842 1.712 1.628 1.610 1.464 1.371 1.242
## Erecta           2 42.7 0.813 0.804 0.871 0.843 0.829 0.844 0.847 0.821 0.834
## GB.97L-Col/99C   3 76.1 3.304 3.249 3.270 3.367 3.438 3.503 3.558 3.663 3.776
## g4539            4 40.3 2.578 2.636 2.674 2.768 2.851 3.014 3.130 3.175 3.324
## c5.loc29         5 29.0 1.710 1.644 1.651 1.688 1.693 1.703 1.673 1.696 1.668
##                  T18  T20   T22   T24   T26   T28   T30   T32   T34   T36   T38
## c1.loc44       1.186 1.13 1.044 0.964 0.908 0.816 0.782 0.666 0.618 0.555 0.507
## Erecta         0.829 0.81 0.801 0.794 0.725 0.701 0.685 0.656 0.624 0.584 0.559
## GB.97L-Col/99C 3.789 3.78 3.813 3.879 3.927 3.939 4.021 4.066 4.067 4.083 4.089
## g4539          3.539 3.65 3.803 3.906 4.175 4.358 4.442 4.594 4.755 4.732 4.852
## c5.loc29       1.671 1.68 1.659 1.673 1.668 1.694 1.673 1.621 1.588 1.612 1.551
##                  T40   T42   T44   T46   T48   T50   T52   T54   T56   T58
## c1.loc44       0.453 0.377 0.368 0.319 0.299 0.279 0.252 0.211 0.192 0.173
## Erecta         0.530 0.508 0.456 0.412 0.411 0.391 0.359 0.324 0.329 0.285
## GB.97L-Col/99C 4.093 4.021 4.003 3.966 3.925 3.788 3.786 3.650 3.591 3.432
## g4539          4.993 5.078 5.148 5.194 5.205 5.207 5.206 5.191 5.284 5.196
## c5.loc29       1.523 1.478 1.442 1.403 1.351 1.299 1.284 1.234 1.161 1.088
##                  T60   T62   T64   T66   T68   T70   T72   T74   T76    T78
## c1.loc44       0.162 0.144 0.129 0.130 0.120 0.106 0.113 0.102 0.101 0.0982
## Erecta         0.290 0.255 0.241 0.230 0.217 0.214 0.206 0.175 0.163 0.1508
## GB.97L-Col/99C 3.339 3.194 3.073 2.917 2.786 2.737 2.567 2.479 2.342 2.2106
## g4539          5.157 5.250 5.222 5.133 5.070 5.027 4.992 4.919 4.853 4.8570
## c5.loc29       1.074 1.042 1.018 0.966 0.922 0.874 0.847 0.814 0.773 0.7299
##                  T80   T82   T84   T86   T88   T90    T92    T94    T96    T98
## c1.loc44       0.103 0.104 0.105 0.098 0.114 0.105 0.1074 0.1172 0.1093 0.1131
## Erecta         0.148 0.132 0.121 0.127 0.119 0.102 0.0914 0.0814 0.0728 0.0601
## GB.97L-Col/99C 2.107 2.005 1.910 1.759 1.686 1.653 1.5581 1.4445 1.3915 1.3201
## g4539          4.708 4.680 4.654 4.628 4.556 4.553 4.4591 4.4817 4.3949 4.3519
## c5.loc29       0.698 0.688 0.652 0.592 0.558 0.566 0.4952 0.5057 0.4541 0.4501
##                 T100   T102   T104   T106   T108   T110  T112   T114   T116
## c1.loc44       0.113 0.1161 0.1200 0.1162 0.1222 0.1318 0.120 0.1385 0.1412
## Erecta         0.054 0.0475 0.0378 0.0359 0.0314 0.0244 0.020 0.0152 0.0156
## GB.97L-Col/99C 1.261 1.2025 1.1539 1.0763 1.0138 0.9615 0.911 0.8466 0.8023
## g4539          4.293 4.1805 4.1896 4.1700 4.0484 4.0468 4.015 3.9296 3.9327
## c5.loc29       0.437 0.4027 0.3911 0.3671 0.3425 0.3148 0.315 0.2957 0.2773
##                  T118  T120    T122    T124    T126    T128    T130    T132
## c1.loc44       0.1360 0.152 0.15143 0.15900 0.17063 0.17645 0.17927 0.18166
## Erecta         0.0111 0.012 0.00674 0.00573 0.00458 0.00189 0.00255 0.00135
## GB.97L-Col/99C 0.7444 0.716 0.65531 0.63656 0.60478 0.54165 0.50956 0.49153
## g4539          3.8783 3.836 3.80218 3.76031 3.70400 3.67065 3.65312 3.58698
## c5.loc29       0.2561 0.261 0.23715 0.22998 0.21391 0.21672 0.19669 0.19553
##                   T134     T136     T138     T140    T142     T144     T146
## c1.loc44       0.18838 0.200526 0.211210 2.23e-01 0.24882 2.53e-01 0.262278
## Erecta         0.00114 0.000444 0.000154 2.97e-05 0.00013 4.98e-05 0.000272
## GB.97L-Col/99C 0.46660 0.441596 0.405226 3.93e-01 0.36492 3.44e-01 0.331472
## g4539          3.56984 3.542344 3.486263 3.40e+00 3.34004 3.31e+00 3.270049
## c5.loc29       0.17890 0.165895 0.162122 1.53e-01 0.14751 1.31e-01 0.125276
##                    T148     T150     T152    T154    T156    T158    T160
## c1.loc44       0.272682 0.279281 0.298193 0.29524 0.29935 0.31341 0.32872
## Erecta         0.000416 0.000885 0.000729 0.00107 0.00189 0.00134 0.00135
## GB.97L-Col/99C 0.308773 0.295154 0.284065 0.28366 0.25365 0.24321 0.23482
## g4539          3.234039 3.224584 3.128876 3.17559 3.12258 3.12157 3.08016
## c5.loc29       0.118572 0.105380 0.093622 0.09015 0.08540 0.07706 0.07418
##                    T162    T164    T166    T168    T170    T172    T174
## c1.loc44       0.323343 0.33839 0.33692 0.36917 0.37012 0.37684 0.39158
## Erecta         0.000837 0.00177 0.00115 0.00167 0.00211 0.00181 0.00177
## GB.97L-Col/99C 0.227744 0.20263 0.19547 0.18645 0.17475 0.17255 0.15555
## g4539          3.050831 2.98835 2.97544 3.00035 2.90233 2.91546 2.87291
## c5.loc29       0.059698 0.05675 0.04845 0.04924 0.03698 0.03344 0.03087
##                    T176    T178    T180     T182     T184     T186     T188
## c1.loc44       0.396246 0.41637 0.42279 0.440910 0.454834 0.493805 0.500053
## Erecta         0.000677 0.00122 0.00151 0.000588 0.000504 0.000524 0.000126
## GB.97L-Col/99C 0.141199 0.13123 0.11933 0.109243 0.105593 0.102506 0.087941
## g4539          2.858374 2.85743 2.81643 2.857460 2.836303 2.810561 2.796151
## c5.loc29       0.028621 0.02706 0.01991 0.017709 0.014348 0.012225 0.009042
##                    T190     T192     T194     T196     T198     T200     T202
## c1.loc44       5.19e-01 5.36e-01 0.559014 0.571271 0.599888 0.621620 6.46e-01
## Erecta         2.96e-06 1.04e-05 0.000147 0.000186 0.000393 0.000881 7.65e-04
## GB.97L-Col/99C 8.29e-02 8.00e-02 0.080873 0.070472 0.058110 0.064294 5.86e-02
## g4539          2.75e+00 2.74e+00 2.748984 2.665410 2.642587 2.619363 2.61e+00
## c5.loc29       8.24e-03 3.89e-03 0.002621 0.001385 0.000198 0.000098 5.42e-05
##                    T204    T206    T208    T210    T212    T214   T216    T218
## c1.loc44       0.655144 0.64923 0.66137 0.68471 0.69256 0.69895 0.6954 0.71307
## Erecta         0.001254 0.00127 0.00169 0.00252 0.00271 0.00213 0.0043 0.00378
## GB.97L-Col/99C 0.055329 0.05135 0.04856 0.04642 0.04204 0.03879 0.0409 0.03847
## g4539          2.550633 2.52180 2.48893 2.48554 2.45776 2.42447 2.3822 2.38851
## c5.loc29       0.000917 0.00213 0.00271 0.00592 0.00642 0.00830 0.0111 0.01282
##                   T220    T222    T224    T226    T228   T230   T232   T234
## c1.loc44       0.72830 0.74445 0.75003 0.78330 0.77869 0.8168 0.8040 0.8103
## Erecta         0.00394 0.00468 0.00463 0.00684 0.00885 0.0111 0.0110 0.0118
## GB.97L-Col/99C 0.03449 0.04053 0.03421 0.03742 0.03981 0.0373 0.0363 0.0360
## g4539          2.37411 2.33230 2.30010 2.29325 2.26351 2.2647 2.2591 2.2438
## c5.loc29       0.01828 0.02038 0.02399 0.02620 0.02934 0.0348 0.0402 0.0434
##                  T236   T238   T240   T242   T244   T246   T248   T250   T252
## c1.loc44       0.8352 0.8480 0.8561 0.8762 0.8780 0.8872 0.8948 0.9196 0.9183
## Erecta         0.0130 0.0167 0.0167 0.0200 0.0194 0.0228 0.0262 0.0295 0.0308
## GB.97L-Col/99C 0.0304 0.0315 0.0319 0.0366 0.0360 0.0348 0.0358 0.0334 0.0350
## g4539          2.2470 2.1782 2.1799 2.1700 2.1314 2.1362 2.0986 2.0560 2.0489
## c5.loc29       0.0480 0.0533 0.0542 0.0587 0.0609 0.0627 0.0719 0.0804 0.0837
##                  T254   T256   T258   T260   T262   T264   T266   T268   T270
## c1.loc44       0.9008 0.8941 0.9243 0.9267 0.9199 0.9424 0.9541 0.9548 0.9635
## Erecta         0.0370 0.0397 0.0429 0.0477 0.0468 0.0550 0.0546 0.0609 0.0634
## GB.97L-Col/99C 0.0300 0.0349 0.0309 0.0319 0.0334 0.0322 0.0340 0.0333 0.0333
## g4539          2.0478 2.0616 2.0569 2.0411 2.0029 1.9944 1.9557 1.9614 1.9281
## c5.loc29       0.0948 0.1004 0.0952 0.1137 0.1279 0.1301 0.1452 0.1434 0.1552
##                  T272   T274   T276   T278   T280  T282   T284   T286   T288
## c1.loc44       0.9628 0.9545 0.9626 0.9998 0.9745 1.003 0.9986 1.0100 1.0142
## Erecta         0.0711 0.0789 0.0856 0.0988 0.0921 0.104 0.1083 0.1167 0.1238
## GB.97L-Col/99C 0.0406 0.0405 0.0464 0.0438 0.0529 0.050 0.0542 0.0544 0.0578
## g4539          1.8790 1.9026 1.8656 1.8545 1.8147 1.830 1.8175 1.8066 1.7481
## c5.loc29       0.1653 0.1681 0.1715 0.1818 0.1915 0.201 0.1959 0.2032 0.2208
##                  T290   T292   T294   T296   T298   T300  T302   T304   T306
## c1.loc44       0.9898 1.0008 1.0239 1.0182 0.9831 1.0024 0.997 1.0115 1.0026
## Erecta         0.1234 0.1249 0.1338 0.1500 0.1433 0.1652 0.170 0.1784 0.1947
## GB.97L-Col/99C 0.0671 0.0545 0.0652 0.0683 0.0705 0.0753 0.073 0.0783 0.0814
## g4539          1.7362 1.7344 1.6823 1.6927 1.6749 1.6671 1.645 1.6013 1.5893
## c5.loc29       0.2181 0.2401 0.2383 0.2418 0.2558 0.2604 0.266 0.2732 0.2872
##                  T308   T310  T312   T314   T316   T318   T320   T322  T324
## c1.loc44       0.9855 0.9901 0.968 0.9642 0.9800 0.9719 0.9473 0.9573 0.908
## Erecta         0.2023 0.2108 0.213 0.2280 0.2234 0.2291 0.2437 0.2607 0.257
## GB.97L-Col/99C 0.0746 0.0822 0.087 0.0876 0.0842 0.0927 0.0972 0.0965 0.102
## g4539          1.5861 1.5758 1.585 1.5521 1.5318 1.5358 1.5023 1.4730 1.445
## c5.loc29       0.2841 0.2971 0.300 0.3155 0.3146 0.3140 0.3330 0.3370 0.373
##                 T326  T328  T330  T332  T334  T336  T338  T340  T342  T344
## c1.loc44       0.900 0.919 0.900 0.900 0.867 0.876 0.891 0.901 0.871 0.879
## Erecta         0.274 0.271 0.285 0.278 0.276 0.301 0.293 0.305 0.316 0.315
## GB.97L-Col/99C 0.101 0.108 0.104 0.104 0.108 0.118 0.110 0.101 0.113 0.117
## g4539          1.443 1.426 1.419 1.406 1.368 1.375 1.366 1.351 1.356 1.330
## c5.loc29       0.385 0.372 0.387 0.391 0.396 0.393 0.406 0.401 0.403 0.409
##                 T346  T348  T350  T352  T354  T356  T358  T360  T362  T364
## c1.loc44       0.879 0.872 0.870 0.842 0.866 0.841 0.819 0.802 0.799 0.778
## Erecta         0.312 0.316 0.319 0.332 0.332 0.357 0.355 0.368 0.366 0.369
## GB.97L-Col/99C 0.113 0.119 0.114 0.115 0.119 0.113 0.116 0.111 0.120 0.119
## g4539          1.324 1.326 1.294 1.319 1.296 1.269 1.264 1.267 1.240 1.207
## c5.loc29       0.412 0.414 0.429 0.423 0.424 0.428 0.447 0.432 0.433 0.440
##                 T366  T368  T370  T372  T374  T376  T378  T380  T382  T384
## c1.loc44       0.764 0.755 0.749 0.703 0.702 0.695 0.661 0.652 0.668 0.627
## Erecta         0.367 0.390 0.386 0.382 0.403 0.381 0.390 0.391 0.400 0.403
## GB.97L-Col/99C 0.125 0.126 0.125 0.118 0.126 0.128 0.129 0.137 0.133 0.139
## g4539          1.184 1.183 1.161 1.148 1.129 1.109 1.116 1.107 1.111 1.094
## c5.loc29       0.443 0.457 0.471 0.484 0.457 0.465 0.459 0.464 0.470 0.468
##                 T386  T388  T390  T392  T394  T396  T398  T400  T402  T404
## c1.loc44       0.619 0.581 0.582 0.570 0.528 0.532 0.510 0.479 0.470 0.448
## Erecta         0.385 0.379 0.390 0.379 0.377 0.363 0.375 0.369 0.353 0.342
## GB.97L-Col/99C 0.143 0.141 0.146 0.147 0.168 0.154 0.164 0.186 0.181 0.183
## g4539          1.063 1.064 1.027 1.045 1.039 1.025 1.014 0.997 0.991 0.956
## c5.loc29       0.456 0.461 0.458 0.454 0.459 0.459 0.442 0.424 0.441 0.453
##                 T406  T408  T410  T412  T414  T416  T418  T420  T422  T424
## c1.loc44       0.442 0.423 0.416 0.413 0.387 0.387 0.366 0.369 0.333 0.331
## Erecta         0.341 0.351 0.337 0.332 0.325 0.319 0.328 0.330 0.333 0.333
## GB.97L-Col/99C 0.195 0.203 0.190 0.195 0.199 0.205 0.203 0.204 0.212 0.216
## g4539          0.937 0.931 0.916 0.920 0.913 0.877 0.858 0.855 0.837 0.845
## c5.loc29       0.429 0.437 0.427 0.429 0.410 0.412 0.404 0.391 0.379 0.376
##                 T426  T428  T430  T432  T434  T436  T438  T440  T442  T444
## c1.loc44       0.309 0.315 0.288 0.275 0.279 0.273 0.255 0.256 0.260 0.243
## Erecta         0.316 0.304 0.300 0.284 0.300 0.297 0.278 0.283 0.267 0.271
## GB.97L-Col/99C 0.217 0.218 0.217 0.218 0.219 0.207 0.212 0.223 0.207 0.214
## g4539          0.831 0.831 0.801 0.767 0.773 0.770 0.737 0.719 0.689 0.697
## c5.loc29       0.402 0.365 0.369 0.358 0.330 0.339 0.316 0.307 0.310 0.297
##                 T446  T448  T450  T452  T454  T456  T458  T460  T462  T464
## c1.loc44       0.237 0.229 0.216 0.212 0.197 0.204 0.198 0.200 0.183 0.192
## Erecta         0.265 0.245 0.259 0.260 0.255 0.242 0.239 0.241 0.240 0.241
## GB.97L-Col/99C 0.226 0.223 0.221 0.217 0.224 0.216 0.222 0.220 0.217 0.217
## g4539          0.674 0.661 0.623 0.625 0.599 0.570 0.547 0.547 0.503 0.471
## c5.loc29       0.289 0.289 0.283 0.270 0.256 0.254 0.260 0.247 0.250 0.251
##                 T466  T468  T470  T472  T474  T476  T478  T480
## c1.loc44       0.190 0.177 0.180 0.171 0.152 0.160 0.154 0.140
## Erecta         0.243 0.230 0.233 0.235 0.212 0.225 0.227 0.218
## GB.97L-Col/99C 0.210 0.216 0.211 0.220 0.203 0.205 0.210 0.214
## g4539          0.455 0.452 0.410 0.417 0.392 0.367 0.353 0.353
## c5.loc29       0.248 0.237 0.244 0.236 0.218 0.229 0.202 0.213

Charts within a loop

If you want to produce multiple interactive charts within a loop.

times_m <- c("T30", "T240", "T480")
times_clear <- c("30 mins", "4 hours", "8 hours")
times_number <- match(times_m, phenames(grav))

for(i in 1:length(times_m))
    print(iplotScanone(out.em, lodcolumn=times_number[i], chartOpts=list(title=times_clear[i])))

That’s it, let’s take a break!

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